Stream Clustering Based on Kernel Density Estimation

We present a novel algorithm for clustering streams of multidimensional points based on kernel density estimates of the data. The algorithm requires only one pass over each data point and a constant amount of space, which depends only on the accuracy of clustering. The algorithm recognizes clusters of nonspherical shapes and handles both inserted and deleted objects in the input stream. Querying the membership of a point in a cluster can be answered in constant time.